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Talk: Vicente Ordonez-Roman
University of North Carolina at Chapel Hill

Title: Language and Perceptual Categorization in Computer Vision

Abstract:
Recently, there has been great progress in both computer vision and natural language processing in representing and recognizing semantic units like objects, attributes, named entities, or constituents. These advances provide opportunities to create systems able to interpret and describe the visual world using natural language. This is in contrast to traditional computer vision systems, which typically output a set of disconnected labels, object locations, or annotations for every pixel in an image. The rich visually descriptive language produced by people incorporates world knowledge and human intuition that often can not be captured by other types of annotations. In this talk, I will present several approaches that explore the connections between language, perception, and vision at three levels: learning how to name objects, generating referring expressions for objects in natural scenes, and producing general image descriptions. These methods provide a framework to augment computer vision systems with linguistic information and to take advantage of the vast amount of text associated with images on the web. I will also discuss some of the intuitions from linguistics and perception behind these efforts and how they potentially connect to the larger goal of creating visual systems that can better learn from and communicate with people.

The popular Microsoft Selfie app has been updated with social sharing to Facebook, Twitter, WeChat as well as native apps for iOS users. The app uses computer vision and machine learning to enhance the images you take, removing noise and auto-correcting for poor lighting.